We set the benchmark for clinical accuracy.

Healthcare records are fragmented, unorganized, and overwhelming. Fourier Health delivers the infrastructure, transparency, and expert oversight you need to make healthcare data trustworthy and actionable.

THE PROBLEM

Healthcare data is messy, unstructured, and often locked in PDFs and faxes, making it hard to act fast or confidently. Traditional AI falls short, missing context or accuracy.

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THE SOLUTION

Fourier Health bridges the gap with an AI-powered platform purpose-built for healthcare—turning raw clinical data into reliable intelligence you can trust and act on.

structured outputschema · cardiology.v3
I10Hypertension, essential.99
E11.9Type 2 diabetes mellitus.97
RxNorm 314076Lisinopril 10 mg.98
LOINC 4548-4Hemoglobin A1c.96

Four layers, working in concert.

Infrastructure, oversight, and integration: built as one system, deployed end to end. Each layer is independently auditable; together they make clinical data finally usable.

Layer 01

Data Infrastructure

We ingest and structure clinical info from any format (notes, faxes, scans) into secure, organized pipelines. The raw becomes routable.

Layer 02

Clinician-in-the-Loop

Expert-verified AI ensures every extraction is clinically accurate. Real clinicians monitor, review, and continuously improve our models.

Layer 03

Real-time Edge Case Reconciliation

Our context-aware AI and human-in-the-loop validation process handles ambiguous records, incomplete data, and complex edge cases to ensure accuracy.

Layer 04

End-to-End Workflow Integration

No more switching tools. Structured insights delivered natively into your EHR and workflows, wherever clinicians already work.

A higher level of data accuracy and transparency.

Generic models miss the mark for healthcare. Our context-rich platform creates far superior results compared to existing solutions and manual intake. We publish our benchmarks.

F1 · clinical extraction benchmarkn=2,400,000 records
Fourier Health
0.96
GPT-class (general)
0.51
Claude-class (general)
0.48
Gemini-class (general)
0.42
Open-source 70B
0.34
Manual intake (avg)
0.28
3.5×

More accurate vs manual intake

F1, weighted across specialty extraction tasks (internal eval, n=2.4M docs)

30×

Higher performing vs frontier labs

Generalist LLMs (GPT-class, Claude-class, Gemini-class) on healthcare-specific schemas

See what your data could be doing for you.

Clinical intelligence you can stake decisions on, not a black box. Talk to our team about what Fourier can do with your data.